Modeling migraine severity with autoregressive ordered probit models

This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. Since ordinal severity measurements arise from a single patient dependencies among the measurements have to be accounted for. For this the autore- gressive ordinal probit (AOP) model of Müller and Czado (2004) is utilized and fitted by a grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler. Initially, covariates are selected using proportional odds models ignoring this dependency. Model fit and model comparison are discussed. The analysis shows that humidity, windchill, sunshine length and pressure differences have an effect in addition to a high dependence on previous measurements. A comparison with proportional odds specifications shows that the AOP models are preferred

Modeling Dynamic Preferences: A Bayesian Robust Dynamic Latent Ordered Probit Model

Much politico-economic research on individuals' preferences is cross-sectional and does not model dynamic aspects of preference or attitude formation. I present a Bayesian dynamic panel model, which facilitates the analysis of repeated preferences using individual-level panel data. My model deals with three problems. First, I explicitly include feedback from previous preferences taking into account that available survey measures of preferences are categorical. Second, I model individuals' initial conditions when entering the panel as resulting from observed and unobserved individual attributes. Third, I capture unobserved individual preference heterogeneity both via standard parametric random effects and a robust alternative based on Bayesian nonparametric density estimation. I use this model to analyze the impact of income and wealth on preferences for government intervention using the British Household Panel Study from 1991 to 2007.</jats:p

Statistical Modeling for Credit Ratings

This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology

INVESTIGATING THE IMPACT OF HEALTH DIFFICULTIES IN ADOLESCENCE ON THE FORMATION OF VALUED ABILITIES

The current adolescent health literature indicates that poor health impairs adolescents’ ability to flourish during adolescence. If an adolescent has a health difficulty, they are more likely to struggle with school attendance and experience social isolation. The human capital literature suggests that cognitive and socioemotional skills developed earlier in life influence the ongoing formation of these skills over time. The implication of these two streams of research is that poor health in adolescence may have a persisting impact the formation of individuals’ valued abilities. In this thesis, I explicitly test this hypothesis. Given the complex nature of the phenomena of interest, I combine both qualitative and quantitative methods. Both approaches support a combined investigation of how health influences individuals’ current and future ability to flourish in the wider aspects of life they consider of inherent value. The findings of the mixed methods research support the hypothesis that a health difficulty in adolescence has a persisting impact on the formation of valued abilities. Poor health in the period prior to GCSE examinations has a continuing impact on individuals’ ability to access education and employment opportunities. It is also associated with an increased risk of having a small friendship network in early adulthood. The accounts of those interviewed with health difficulties indicate they sought to overcome the constraints imposed by their health difficulty. However, their poor experienced health often led to a negative sense of “difference” – undermining a positive sense of self. A health difficulty in adolescence disrupts an individual’s current and future ability to enjoy a number of valued abilities. To efficiently allocate health care resources, policy makers should consider whether a greater priority needs to be apportioned to alleviating the poor experienced health of those populations in which these valued abilities are still at a highly formative stage

Epidemiology of diabetes and related mortality: early screening, socioecological determinants, and the value of prevention

The focus of my dissertation is prediction, prevention, and economic valuation of type 2 diabetes. I studied individual level type 2 diabetes risk factors, spatial spillover effect of diabetes-related mortality ecological risk factors, and individuals’ loss of well-being due to diabetes. In the first essay, titled “Diabetes Risk Prediction: Multivariate Nonlinear Interaction Approach,” I argue that the success in preventing or delaying the incidence of type 2 diabetes and subsequent complications depend on the early detection of undiagnosed cases and identifying people at high-risk. However, early detection of type 2 diabetes is seldom feasible because the symptoms show up late, and screening the entire population is very costly. Individuals who are prone (e.g., due to family history) to developing type 2 diabetes and those with undiagnosed diabetes need to be targeted for early screening. Thus, it is imperative to continue designing assessment mechanisms that help to identify individuals at high-risk based on simple, non-invasive, inexpensive, and routine clinical measurements. In this paper, I build a model that helps to predict type 2 diabetes with readily available, inexpensive, non-invasive, and easy-to-collect information. National Health and Nutrition Examination Survey (NHANES) data is analyzed to build this risk model. A non-parametric regression method, Multivariate Adaptive Regression Splines (MARS), is used to allow for interactions and non-linearity in the model. A risk prediction model using the MARS approach achieved a performance level of 87% accuracy with area under receiver operating character curve (AUROC) of 0.86, which is higher than similar models based on invasive and non-invasive measurements. Moreover, this model requires few measurements and limited information that may be obtained in settings such as community-based chronic disease prevention programs and workplace well-being programs. Therefore, this risk prediction model can be translated into a usable risk-scoring tool in community chronic prevention and employee wellness programs. The second essay, titled “Spatial Spillover Effect from Socio-Ecological Determinants of Diabetes-Related Mortality in the US,” explores the spatial spillover effect from socio-ecological risk factors that are associated with type 2 diabetes-related mortality. I studied the spatial spillover effect of change in socioeconomic gradients (education, employment, and household income), retail food environments, and access to health-care on diabetes-related mortality rates (DRMR) across the United States. To examine mortality clusters and factors associated with the clusters and spatial spillover effect, seven-year aggregates of multiple-cause mortality data from CDC WONDER compressed mortality database was merged with several sources of county-level data. The results show that high DRMR cluster counties are located throughout the Southern Plains, Southeastern, and Appalachian regions. High DRMR clusters are characterized by lower socioeconomic status, high density of fast food restaurants, lack of access to grocery stores, high proportion of African Americans, and low physical activity. Moreover, the impacts from change in socioeconomic gradients and the retail food environment in a particular county spill over to neighboring counties. The implication is that improvement in socioeconomic status and access to healthy food would significantly reduce DRMR in contiguous US counties. The third essay, titled “What is the Value in Diabetes Prevention? A Subjective Well-Being Valuation Approach,” uses loss of well-being due to diabetes to quantify the monetary value of diabetes prevention in the US population. In this paper, I argue that the current preference-based health valuation approach is not appropriate for prevention-based programs valuation because they do not capture the social and economic value that an individual puts on a health condition. I utilize a recently developed subjective well-being valuation approach to quantify the monetary value of loss in well-being due to diabetes in the US population. This approach assumes that individuals derive overall life satisfaction from well-being, which is a function of health and income. Health, in turn, is produced by the combined input of an individual’s behaviors and medical technology. Thus, a marginal trade-off between health and income is used to derive the monetary value of health. The Panel Study of Income Dynamics (PSID) data was utilized for this study. The result shows that the monetary value for diabetes prevention is about $37,000, which is less than the current implicit threshold for program implementation. The resulting monetary value will help to quantify the societal value of diabetes prevention, which can be used to estimate the benefit side of the cost-benefit analysis

The impact of crowding in emergency departments on patient health and health outcomes : an instrumental variable analysis

It is commonly presumed that emergency department (ED) crowding results in the delivery of inferior quality health care and deleterious effects for acute patients. Yet a growing body of literature assessing outcomes for patients treated under crowded conditions has yielded inconsistent results. This dissertation identifies whether and to what extent crowding in hospital-based EDs has a causal impact on health outcomes. The conflicting evidence stems from both conceptual and empirical weaknesses in the crowding literature. Conceptually, no universal definition of crowding exists. Although most frequently defined as the incapacity to meet demand for ED services, there is a lack of consistency in how capacity is related to crowding (e.g., number of arrivals, occupants, proportion of unserved patients, etc.). The wide variation in conceptualization contributes to the use of myriad proxies to estimate crowding’s effect and to a rudimentary understanding of its impact. Additionally, studies measure an assortment of outcomes with discord among researchers over the most appropriate to assess causality. Policy and program recommendations have been advanced to mitigate crowding based on the perception that it is a problem, but evidence-based research reveals incongruency in the findings. Furthermore, empirical studies have not identified any more than an associative crowding-outcomes relationship, challenging inferences made about crowding’s true effects. This is primarily due to insufficient controls for endogenous variables, contributing to biased estimators. The obscurity in whether crowding affects patient health fuels debate about the underlying issue, consequences thereof, and efficacious and cost-effective strategies to address it. This dissertation aims to: (1) offer a new, standardizable tool to define and more accurately measure the degree of crowding; and (2) assess the relationship between ED crowding and health outcomes using a new construct of crowding – “patient arrival density,” to instrument for ED occupancy rate. This empirical study employs an instrumental variable estimation approach to evaluate the effect of crowding on probability of mortality, severe sepsis, diabetic ketoacidosis, and subsequent ED visits. Findings show crowding has no causal effect on any of these outcomes, suggesting resources targeted at reducing crowding in EDs may be better directed at identifying the true mechanisms that can lead to sentinel events. By taking a novel approach, this dissertation advances existing research and contributes to our knowledge of the crowding phenomenon, which subsequently can help better direct resources and future research aimed at improving the delivery of care in the acute healthcare system.Public Affair